Graphical Models
Methods for Data Analysis and Mining
(Sprache: Englisch)
Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data
All basic concepts carefully explained and illustrated by examples throughout
Contains background material including graphical...
All basic concepts carefully explained and illustrated by examples throughout
Contains background material including graphical...
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Produktinformationen zu „Graphical Models “
Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data
All basic concepts carefully explained and illustrated by examples throughout
Contains background material including graphical representation, including Markov and Bayesian Networks.
Includes a comprehensive bibliography.
Second edition features more extensive coverage of clique tree propagation, visualization techniques and exercises for the reader, the book is also supported by a website containing additional material.
All basic concepts carefully explained and illustrated by examples throughout
Contains background material including graphical representation, including Markov and Bayesian Networks.
Includes a comprehensive bibliography.
Second edition features more extensive coverage of clique tree propagation, visualization techniques and exercises for the reader, the book is also supported by a website containing additional material.
Klappentext zu „Graphical Models “
Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.
Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.
Inhaltsverzeichnis zu „Graphical Models “
Preface.1 Introduction.
1.1 Data and Knowledge.
1.2 Knowledge Discovery and Data Mining.
1.3 Graphical Models.
1.4 Outline of this Book.
2 Imprecision and Uncertainty.
2.1 Modeling Inferences.
2.2 Imprecision and Relational Algebra.
2.3 Uncertainty and Probability Theory.
2.4 Possibility Theory and the Context Model.
3 Decomposition.
3.1 Decomposition and Reasoning.
3.2 Relational Decomposition.
3.3 Probabilistic Decomposition.
3.4 Possibilistic Decomposition.
3.5 Possibility versus Probability.
4 Graphical Representation.
4.1 Conditional Independence Graphs.
4.2 Evidence Propagation in Graphs.
5 Computing Projections.
5.1 Databases of Sample Cases.
5.2 Relational and Sum Projections.
5.3 Expectation Maximization.
5.4 Maximum Projections.
6 Naive Classifiers.
6.1 Naive Bayes Classifiers.
6.2 A Naive Possibilistic Classifier.
6.3 Classifier Simplification.
6.4 Experimental Evaluation.
7 Learning Global Structure.
7.1 Principles of Learning Global Structure.
7.2 Evaluation Measures.
7.3 Search Methods.
7.4 Experimental Evaluation.
8 Learning Local Structure.
8.1 Local Network Structure.
8.2 Learning Local Structure.
8.3 Experimental Evaluation.
9 Inductive Causation.
9.1 Correlation and Causation.
9.2 Causal and Probabilistic Structure.
9.3 Faithfulness and Latent Variables.
9.4 The Inductive Causation Algorithm.
9.5 Critique of the Underlying Assumptions.
9.6 Evaluation.
10 Visualization.
10.1 Potentials.
10.2 Association Rules.
11 Applications.
11.1 Diagnosis of Electrical Circuits.
11.2 Application in
... mehr
Telecommunications.
11.3 Application at Volkswagen.
11.4 Application at DaimlerChrysler.
A Proofs of Theorems.
A.1 Proof of Theorem 4.1.2.
A.2 Proof of Theorem 4.1.18.
A.3 Proof of Theorem 4.1.20.
A.4 Proof of Theorem 4.1.26.
A.5 Proof of Theorem 4.1.28.
A.6 Proof of Theorem 4.1.30.
A.7 Proof of Theorem 4.1.31.
A.8 Proof of Theorem 5.4.8.
A.9 Proof of Lemma .2.2.
A.10 Proof of Lemma .2.4.
A.11 Proof of Lemma .2.6.
A.12 Proof of Theorem 7.3.1.
A.13 Proof of Theorem 7.3.2.
A.14 Proof of Theorem 7.3.3.
A.15 Proof of Theorem 7.3.5.
A.16 Proof of Theorem 7.3.7.
B Software Tools.
Bibliography.
Index.
11.3 Application at Volkswagen.
11.4 Application at DaimlerChrysler.
A Proofs of Theorems.
A.1 Proof of Theorem 4.1.2.
A.2 Proof of Theorem 4.1.18.
A.3 Proof of Theorem 4.1.20.
A.4 Proof of Theorem 4.1.26.
A.5 Proof of Theorem 4.1.28.
A.6 Proof of Theorem 4.1.30.
A.7 Proof of Theorem 4.1.31.
A.8 Proof of Theorem 5.4.8.
A.9 Proof of Lemma .2.2.
A.10 Proof of Lemma .2.4.
A.11 Proof of Lemma .2.6.
A.12 Proof of Theorem 7.3.1.
A.13 Proof of Theorem 7.3.2.
A.14 Proof of Theorem 7.3.3.
A.15 Proof of Theorem 7.3.5.
A.16 Proof of Theorem 7.3.7.
B Software Tools.
Bibliography.
Index.
... weniger
Autoren-Porträt von Christian Borgelt, Matthias Steinbrecher, Rudolf R Kruse
Prof. Dr. Rudolf Kruse ist Leiter des Lehrstuhls für Neuro-Fuzzy-Systeme an der Universität Magdeburg.
Bibliographische Angaben
- Autoren: Christian Borgelt , Matthias Steinbrecher , Rudolf R Kruse
- 2009, 2. Aufl., 416 Seiten, mit Abbildungen, Maße: 15,5 x 22,9 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 047072210X
- ISBN-13: 9780470722107
- Erscheinungsdatum: 17.08.2009
Sprache:
Englisch
Pressezitat
"The text provides graduate students, and researchers withall the necessary background material, including modelling underuncertainty, decomposition of distributions, graphicalrepresentation of distributions, and applications relating tographical models and problems for further research." ( Zentralblatt Math , 1 August 2013)"All of the necessary background is provided, with material onmodeling under uncertainty and imprecision modeling, decompositionof distributions, graphical representation of distributions,applications relating to graphical models, and problems for furtherresearch." ( Book News , December 2009)
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